International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Deep Learning Model for UG Student Performance Evaluation in E-Learning

Author(s) Ms. SWATI SHARAD SAPKALE, Prof. Dr. GAJENDRA RAMDAS WANI
Country India
Abstract The rapid proliferation of e-learning environments across higher education, especially due to the global digitalization of education and the adoption of Learning Management Systems (LMSs), has resulted in vast amounts of student learning data being generated daily12. Analyzing this data to predict the academic performance of undergraduate students is critical for timely interventions, personalization, and the overall effectiveness of virtual learning platforms3. Deep learning, with its data-driven approach and ability to model complex, non-linear relationships, has become an influential tool in student performance prediction, outperforming many traditional machine learning algorithms in recent years4.
This comprehensive guide presents a detailed, step-by-step approach for developing a small-scale deep learning model to predict or assess student performance in an e-learning context. Covering the full pipeline-from data collection and preprocessing, through model construction and evaluation, to deployment and ethical considerations-it synthesizes recent academic research and best practices for educational data mining. The aim is to empower educators, researchers, and developers with a clear, actionable methodology suitable for small-scale implementation in real-world academic environments.
Keywords Strategies in E-Learning Contexts Understanding Data Sources: Direct vs. Indirect Evidence
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-10-28
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.59022
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